{"id":"W3035110996","doi":"10.21203/rs.3.rs-36564/v1","title":"Deep learning of stochastic contagion dynamics on complex networks","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Complex network; Dynamics (music); Computer science; Emotional contagion; Artificial intelligence; Statistical physics; Econometrics; Economics; Psychology; Physics; Neuroscience; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008425531,0.0009550106,0.001912591,0.002254418,0.001205121,0.0006306994,0.003027256,0.0006115811,0.002049868],"category_scores_gemma":[0.000833353,0.0009871792,0.001065169,0.003271185,0.001332179,0.0001539119,0.00620213,0.01455607,0.0002238805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001264802,"about_ca_system_score_gemma":0.0008563896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004513652,"about_ca_topic_score_gemma":0.0003386265,"domain_scores_codex":[0.9813228,0.006277283,0.001502911,0.002252169,0.005696337,0.002948455],"domain_scores_gemma":[0.9864425,0.005520248,0.0005641002,0.00232009,0.004167526,0.0009855058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001457469,0.002159631,0.0196542,0.001884751,0.001828985,0.0001158524,0.001778698,0.6192824,0.0003848825,0.2090805,0.02289709,0.1194756],"study_design_scores_gemma":[0.0006987047,0.001453843,0.002944478,0.001568873,0.00006327422,9.531415e-7,0.001996496,0.9466543,0.00007457328,0.04222607,0.001540401,0.0007780581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0308718,0.0009959195,0.9374031,0.002479517,0.0002772681,0.006858645,0.0003440214,0.0006362811,0.02013341],"genre_scores_gemma":[0.9924588,0.0001394353,0.0008337808,0.00001965722,0.001798503,0.000865705,0.003186028,0.0002611411,0.0004369452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.961587,"threshold_uncertainty_score":0.9992579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1069310045670926,"score_gpt":0.4230319305828925,"score_spread":0.3161009260157999,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}